The Internet-of-Things (IoT), or the Internet of Everything, is a network of physical objects or “things” that can be segmented into, for example, wearables, connected cars, connected homes, connected cities, and industrial Internet/networks. A large amount of data can be quickly generated in each of these segments and can be used to improve lives of both individuals and groups/organizations, especially if useful information/actionable insights can be “learned” or “discovered” in real-time for maximum impact.
In many manufacturing contexts, knowledge empowers preservation of valuable heritage, new learning, solving intricate problems, creating core competencies, and initiating new situations for both individuals and organizations now and in the future. In most sectors, manufacturing can be extremely competitive. Financial margins that differentiate between success and failure are very tight, with most established industries needing to compete, produce, and sell at a global level. To master these trans-continental challenges, an organization must achieve low-cost production yet still maintain highly skilled, flexible, and efficient workforces that are able to consistently design and produce high-quality and low-cost products.
In modern manufacturing, the volume of data grows at an unprecedented rate in digital manufacturing environments with the use of barcodes, sensors, vision systems, etc. This data may be, for example, related to design, products, machines, processes, materials, inventories, maintenance, planning and control, assembly, logistics, performances, etc., and may include patterns, trends, associations, and dependencies. However, the use of accumulated data has been limited, which has led to what may be called a “rich data but poor information” problem.
The huge amounts of data stored in manufacturing databases, which often contain large numbers of records with many attributes that need to be simultaneously explored/analysed to discover useful information, make manual analysis impractical. All these factors indicate the need for intelligent and automated data analysis methodologies that discover/reveal useful knowledge from data. Knowledge discovery in databases (KDD) and data mining (DM) have therefore become important tools in realizing an objective of intelligent and automated data analysis.